Structure Representation Generation

<div align="center">

Atomic Structure Generation from Reconstructing Structural Fingerprints

[arXiv]

</div>

Requirements

Git clone this repo and in the main directory do

pip install -r requirements.txt
pip install -e .

The package has been tested on

• Python 3.6.10 / 3.9.12
• PyTorch 1.10.2 / 1.12.0
• Torch.cuda 11.3

How to Use

Most configurations of the system/code are done through .yaml files under configs/.

To load a configuration:

# load our config yaml file
stream = open('./configs/example/example_extract_reconstruct.yaml')
CONFIG = yaml.safe_load(stream)
stream.close()
# dotdict for dot operations on Python dict 
# e.g., CONFIG.cutoff == CONFIG['cutoff']
CONFIG = dotdict(CONFIG)

Representation Extraction

Extract representations for training generative model using selected descriptor.

See examples/example_extract.py

Generative Model

Train a CVAE (conditional variational auto-encoder) as the generative model.

See examples/example_cvae_trainer.py.

Generation

Generate representation from a given target value using the decoder part of the CVAE.

See examples/example_generator.py.

Reconstruction

Generate atomic structures from generated representation.

See examples/example_reconstruction.py.

A script to run all of the above examples is provided in quickrun.sh. To execute, do

chmod u+x quickrun.sh
./quickrun.sh

---

Citation

@article{fung2022atomic,
  title={Atomic structure generation from reconstructing structural fingerprints},
  author={Fung, Victor and Jia, Shuyi and Zhang, Jiaxin and Bi, Sirui and Yin, Junqi and Ganesh, P},
  journal={arXiv preprint arXiv:2207.13227},
  year={2022}
}